(668g) Development of Protocol for Structure-to-Properties Studies of Non-Platinum Electrocatalysts by Multivariate Analysis and Modeling of Xps Spectroscopic Data

The methodology of production of a new class of non-platinum electrocatalysts, based on pyrolysed porphyrins, was evaluated using X-ray photoelectron spectroscopy (XPS) in combination with multivariate analysis. Understanding the composition of active catalysts sites enables correlation of chemical composition with electrochemical measurements, which is critical for both optimizing the catalyst performance and elucidating failure mechanisms. XPS is frequently used for structural studies of fuel cell components. Spectral interpretation and extraction of quantitative information on the distribution of chemical moieties can be quite challenging, however, due to ambiguities in the deconvolution of the XPS spectra, which contain multiple overlapping components. Here we present the protocol for characterization of changes in surface chemistry occurring during catalyst creation, modification and aging. The effects of the initial structure of the precursor, pyrolysis temperature, the temperature gradient during pyrolysis, cooling parameters and various types of chemical post-treatment are among the parameters followed in this study. Changes in the chemical structure of the electrocatalyst before and after use in a fuel cell were also included in the analysis. The methodology has been applied to both non-platinum CoTMPP electrocatalysts, based on pyrolysed porphyrins, and carbon blacks used as catalyst supports. The developed protocol utilizes conventional analysis by XPS in combination with multivariate analysis techniques, facilitating both interpretation of the data and the development of structure-to-property correlations. Principal component analysis (PCA) was applied to XPS spectra that were deconvolved through a curve-fitting procedure. PCA searches for correlation and variation in the quantitative distribution of chemical species as a function of catalyst production and treatment, enabling identification of chemical moieties present as active sites and correlation with electrochemical performance. Multivariate regression was used to develop a spectra-to-property relationship by correlating XPS and electrochemical data. Models developed based on this study can potentially be utilized to predict the chemical composition of catalysts with optimized electrochemical performance. The methodology applied in this study can, therefore, be effectively used to identify active catalytic sites responsible for oxidation/reduction process, detect chemical species responsible for corrosion of the catalyst material, and assist in design of optimized electrocatalysts.